TY - JOUR
T1 - Phenobot
T2 - 44th World Congress of Vine and Wine, OIV 2023
AU - Martins, R. C.
AU - Cunha, M.
AU - Santos, F.
AU - Tosin, R.
AU - Barroso, T. G.
AU - Silva, F.
AU - Queirós, C.
AU - Pereira, M. R.
AU - Moura, P.
AU - Pinho, T.
AU - Boaventura, J.
AU - Magalhães, S.
AU - Aguiar, A. S.
AU - Silvestre, J.
AU - Damásio, M.
AU - Amador, R.
AU - Barbosa, C.
AU - Martins, C.
AU - Araújo, J.
AU - Vidal, J. P.
AU - Rodrigues, F.
AU - Maia, M.
AU - Rodrigues, V.
AU - Garcia, A.
AU - Raimundo, D.
AU - Trindade, M.
AU - Pestana, C.
AU - Maia, P.
N1 - Publisher Copyright:
© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).
PY - 2023/12/6
Y1 - 2023/12/6
N2 - The Phenobot platform is comprised by an autonomous robot, instrumentation, artificial intelligence, and digital twin diagnosis at the molecular level, marking the transition from pure data-driven to knowledge-driven agriculture 4.0, towards a physiology-based approach to precision viticulture. Such is achieved by measuring the plant metabolome 'in vivo' and 'in situ', using spectroscopy and artificial intelligence for quantifying metabolites, e.g.: i. grapes: chlorophylls a and b, pheophytins a and b, anthocyanins, carotenoids, malic and tartaric acids, glucose and fructose; ii. foliage: chlorophylls a and b, pheophytins a and b, anthocyanins, carotenoids, nitrogen, phosphorous, potassium, sugars, and leaf water potential; and iii. soil nutrients (NPK). The geo-referenced metabolic information of each plant (organs and tissues) is the basis of multi-scaled analysis: i. geo-referenced metabolic maps of vineyards at the macroscopic field level, and ii. genome-scale 'in-silico' digital twin model for inferential physiology (phenotype state) and omics diagnosis at the molecular and cellular levels (transcription, enzyme efficiency, and metabolic fluxes). Genome-scale 'in-silico' Vitis vinifera numerical network relationships and fluxes comprise the scientific knowledge about the plant's physiological response to external stimuli, being the comparable mechanisms between laboratory and field experimentation - providing a causal and interpretable relationship to a complex system subjected to external spurious interactions (e.g., soil, climate, and ecosystem) scrambling pure data-driven approaches. This new approach identifies the molecular and cellular targets for managing plant physiology under different stress conditions, enabling new sustainable agricultural practices and bridging agriculture with plant biotechnology, towards faster innovations (e.g. biostimulants, anti-microbial compounds/mechanisms, nutrition, and water management). Phenobot is a project under the Portuguese emblematic initiative in Agriculture 4.0, part of the Recovery and Resilience Plan (Ref. PRR: 190 Ref. 09/C05-i03/2021 - PRR-C05-i03-I-000134).
AB - The Phenobot platform is comprised by an autonomous robot, instrumentation, artificial intelligence, and digital twin diagnosis at the molecular level, marking the transition from pure data-driven to knowledge-driven agriculture 4.0, towards a physiology-based approach to precision viticulture. Such is achieved by measuring the plant metabolome 'in vivo' and 'in situ', using spectroscopy and artificial intelligence for quantifying metabolites, e.g.: i. grapes: chlorophylls a and b, pheophytins a and b, anthocyanins, carotenoids, malic and tartaric acids, glucose and fructose; ii. foliage: chlorophylls a and b, pheophytins a and b, anthocyanins, carotenoids, nitrogen, phosphorous, potassium, sugars, and leaf water potential; and iii. soil nutrients (NPK). The geo-referenced metabolic information of each plant (organs and tissues) is the basis of multi-scaled analysis: i. geo-referenced metabolic maps of vineyards at the macroscopic field level, and ii. genome-scale 'in-silico' digital twin model for inferential physiology (phenotype state) and omics diagnosis at the molecular and cellular levels (transcription, enzyme efficiency, and metabolic fluxes). Genome-scale 'in-silico' Vitis vinifera numerical network relationships and fluxes comprise the scientific knowledge about the plant's physiological response to external stimuli, being the comparable mechanisms between laboratory and field experimentation - providing a causal and interpretable relationship to a complex system subjected to external spurious interactions (e.g., soil, climate, and ecosystem) scrambling pure data-driven approaches. This new approach identifies the molecular and cellular targets for managing plant physiology under different stress conditions, enabling new sustainable agricultural practices and bridging agriculture with plant biotechnology, towards faster innovations (e.g. biostimulants, anti-microbial compounds/mechanisms, nutrition, and water management). Phenobot is a project under the Portuguese emblematic initiative in Agriculture 4.0, part of the Recovery and Resilience Plan (Ref. PRR: 190 Ref. 09/C05-i03/2021 - PRR-C05-i03-I-000134).
UR - http://www.scopus.com/inward/record.url?scp=85184617721&partnerID=8YFLogxK
U2 - 10.1051/bioconf/20236801018
DO - 10.1051/bioconf/20236801018
M3 - Conference article
AN - SCOPUS:85184617721
SN - 2273-1709
VL - 68
SP - 1
EP - 6
JO - BIO Web of Conferences
JF - BIO Web of Conferences
M1 - 01018
Y2 - 5 June 2023 through 9 June 2023
ER -
